
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA suspicious (short/empty) | Behavioral anomaly detected by automated analysis | +15 | |
| Danger strong hits: 4 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 176 | Medium-risk: admin panels, config files | +60 | |
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Probe pattern 302->404 same path | Behavioral anomaly detected by automated analysis | +20 | |
| Burst: 50 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 126 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 54 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 164 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 8 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 264 | Medium-risk: admin panels, config files | +60 | |
| Burst: 46 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 127 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 6 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Burst: 40 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 122 req / 10s | Abnormally fast request rate — automated scanning | +35 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 4.225.204.159 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
IP 4.225.204.159 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
IP 4.225.204.159 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
This IP was checked against major DNS-based blacklists used by mail servers and firewalls worldwide.
Checked: Spamhaus, SpamCop, Barracuda, SORBS, CBL, UCEProtect. Results may change over time.
4.225.204.159 has been assigned a threat score of 280/100 (Critical). This is a critical-level threat. Systems administrators should treat this IP as hostile and block all inbound connections without exception.
The following attack categories were identified:
Threat intelligence analysis has linked 4.225.204.159 to malicious activity originating from Gävle, Sweden, operating on the network of Microsoft Corporation. The address has been under observation since its initial detection. Over a period of 1 days, this IP generated 4 malicious requests, averaging approximately 4 requests per day. Operating from datacenter infrastructure, this IP is typical of addresses used in organized attack operations. Cloud and VPS providers are commonly exploited as launching platforms for automated scanning. With 3 different attack patterns detected, this IP exhibits behavior characteristic of advanced automated scanning frameworks. With 102 flagged addresses, Sweden represents a significant presence in our threat database. With a threat score of 280/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
This IP belongs to a hosting or data center provider. Malicious traffic from hosting infrastructure often originates from compromised VPS instances, rented servers used for scanning campaigns, or abused free-tier cloud accounts. Hosting providers typically respond to abuse reports within 24-72 hours.
Examining HTTP headers beyond User-Agent reveals attack tools and automated scripts. Missing standard headers, unusual ordering, non-standard values, and inconsistencies with claimed client identity all serve as reliable detection signals.
Advanced techniques enable threat detection while minimizing privacy impact. Encrypted DNS, differential privacy in analytics, and federated learning for threat models allow effective security monitoring without unnecessary surveillance of legitimate user behavior.